Data Selection Based on Uncertainty Quantification

2. The computer system according to claim 1, wherein the first estimator is a first neural network and the second estimator is a second neural network.

3. The computer system according to claim 2, wherein the first neural network and the second neural network are equivalent neural networks, the first neural network trained using a first data set and the second neural network trained using a second data set, the first data set being different than the second data set.

4. The computer system according to claim 2, wherein the first neural network and the second neural network are different neural networks, the first neural network and the second neural network trained using a common data set.

5. The computer system according to claim 1, wherein performing the first comparison generates a first value representing a first uncertainty quantification and performing the second comparison generates a second value representing a second uncertainty quantification.

6. The computer system according to claim 1, wherein the first results comprise a first bounding box and a second bounding box and the second results comprise a third bounding box and a fourth bounding box, the first and third bounding boxes generated by the first estimator and the second and fourth bounding boxes generated by the second estimator.

10. The device according to claim 9, wherein the first viewpoint is different from the second viewpoint.

13. The device according to claim 9, wherein determining the first value comprises calculating an average disagreement between discrete image data portions associated with the first set of image data, and wherein determining the second value comprises calculating an average disagreement between discrete image data portions associated with the second set of image data.

14. The device according to claim 9, wherein the object viewed from the first viewpoint is captured by a camera associated with the device and the object viewed from the second viewpoint is captured by the camera associated with the device.

16. The computer-implemented method according to claim 15, wherein individual estimators of the estimators are implemented by a neural network.

17. The computer-implemented method according to claim 16, wherein individual neural networks of the neural networks are implemented by an equivalent neural network, and each neural network of the neural networks is trained with a distinct data set.

18. The computer-implemented method according to claim 16, wherein individual neural networks of the neural networks are different neural networks, and each neural network of the neural networks is trained with a common data set.

19. The computer-implemented method according to claim 15, wherein the plurality of evaluations comprise evaluations generated based on the outputs of the estimators comprising at least object poses generated by the estimators based on the image data representing the object.

20. The computer-implemented method according to claim 19, wherein the object poses comprise bounding cuboids associated with the object.

22. The non-transitory machine-readable medium according to claim 21, wherein the plurality of estimators comprise neural networks.

23. The non-transitory machine-readable medium according to claim 21, wherein individual estimators of the plurality of estimators are implemented by an equivalent estimator, and each estimator of the plurality of estimators is trained with a distinct data set.

24. The non-transitory machine-readable medium according to claim 21, wherein individual estimators of the plurality of estimators are different estimators, and each estimator of the plurality of estimators is trained with a common data set.

25. The non-transitory machine-readable medium according to claim 21, wherein the image data comprises image data of the object from a first viewpoint and image data of the object from a second viewpoint, and the portion of the image data comprises the image data of the object from the first viewpoint or the image data of the object from the second viewpoint.

27. The non-transitory machine-readable medium according to claim 21, wherein the poses of the first and second sets of poses comprise bounding boxes generated by the plurality of estimators.

29. The computer system according to claim 28, wherein the first estimator is a first neural network and the second estimator is a second neural network.

30. The computer system according to claim 29, wherein the first neural network and the second neural network are equivalent neural networks, the first neural network trained using a first data set and the second neural network trained using a second data set, the first data set being different than the second data set.

31. The computer system according to claim 29, wherein the first neural network and the second neural network are different neural networks, the first neural network and the second neural network trained using a common data set.

32. The computer system according to claim 28, wherein performing the first comparison generates a first value representing a first uncertainty quantification and performing the second comparison generates a second value representing a second uncertainty quantification.

33. The computer system according to claim 28, wherein the first results comprise a first bounding box and a second bounding box and the second results comprise a third bounding box and a fourth bounding box, the first and third bounding boxes generated by the first estimator and the second and fourth bounding boxes generated by the second estimator.